Pattern formation arising from the collective behaviour of autonomous agents occurs across many areas of biology, including skin patterns. Agent-based models provide a natural framework for describing such systems. However, the high-dimensional nature of the data and model stochasticity pose significant challenges for parameter inference and identifiability analysis. To help address this...
Tumors grow and metastasize through intricate interactions among the diverse signals and cell types composing the tumor microenvironment (TME). Cancer systems immunology combines mathematical modeling with analysis of high-dimensional multi-modal data and machine learning to gain insight into the complex ecosystems created by the immune system in and around a tumor. Immunotherapies such as...
Artificial Intelligence Virtual Cell (AIVC) is increasingly emerging as a frontier in the interdisciplinary integration of biology and artificial intelligence. Its core vision is to construct digital twins capable of simulating and predicting the dynamic evolution of cellular states, thereby providing computational support for experimental design and mechanistic analysis. We have explored a...
Real-world biomedical time-series data, including clinical records and measurements of complex biological phenomena, contain valuable insights into disease progression and underlying mechanisms. However, such data are often noisy, irregularly sampled, and partially observed, posing significant challenges for analysis. Extracting latent temporal structures from these observations is therefore...
Electrical impedance tomography (EIT) is a medical imaging technique that uses electric currents and potential measurements on the surface of the body to infer the electrical conductivity within the body. To improve the reconstructed image, our models of biological tissues incorporate anisotropic conductivity, the electrophysiology of electrically active tissues, and the physics of ionic...
Complex disease mechanisms in medical research are often inferred from limited patient samples, posing fundamental challenges in capturing inter-individual variability and selecting optimal therapeutic strategies. To address this limitation, I propose a new methodological framework, termed a multi-omics methodological framework, that systematically integrates mechanistic mathematical models...
The reliability of single-cell RNA sequencing (scRNA-seq) analysis is often hindered by subjective parameter tuning and stochastic inconsistencies, which pose significant challenges for the reproducibility of large-scale studies. To overcome these limitations and establish a rigorous foundation for single-cell foundation models, we propose a fully data-driven analysis framework based on...
Agent-based models (ABMs) capture heterogeneous contacts, stochastic transmission, and complex interventions in infectious disease dynamics but are computationally expensive, limiting parameter inference and policy analysis. We develop an equation-learning framework that derives interpretable ordinary differential equation (ODE) surrogates directly from stochastic ABM simulations. Using the...
Quantitative experimental methods in the life sciences have advanced rapidly in recent years. The emergence of multi-omics, organoid systems, and in vivo live imaging has placed data at the center of modern biological discovery, driving the rapid evolution of data science for extracting biologically meaningful insights.
In parallel, mathematical modeling in the life sciences is shifting...
